Research on the Path and Training System for Enhancing AI Teaching Application Capability for Vocational Education Teachers
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Abstract
This paper addresses three typical problems encountered by vocational education teachers in AI-assisted teaching: unclear conceptual understanding, weak practical operation ability, and difficulty in innovative application. A three-in-one improvement system of “competency framework-training content-implementation path” is constructed. First, based on literature analysis and the Delphi method, a four-dimensional competency framework is developed, covering awareness and cognition, tool operation, instructional design, and ethical evaluation. Second, a modular training content system featuring tiered application and integration of theory and practice is designed according to this framework. Third, a cyclical empowerment path of “research-training-application-evaluation” is constructed. The system is validated through quasi-experimental research and longitudinal tracking. Results show that the training system significantly improves teachers’ AI teaching application capability, with the mean scale score increasing from 2.79 to 3.78, representing a relative increase of 35.5% and Cohen’s d of 1.78. The constructed scale shows good reliability and validity, with total α = 0.934. Longitudinal tracking also reveals attenuation in tool-use frequency and innovative applications, indicating the need for sustained institutional support.
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